Seeing is Believing. Which is Dangerous.

One of the nice things about being at EMBL is that, if you just wait, eventually you can hear the important people in your field speak. Today, I’m quite excited about the Seeing is Believing conference

But ever since I saw this advertised, I dislike the name Seeing is Believing.


  1. Seeing is believing. This is unquestionable.
  2. But seeing is not always justified believing. Our seeing apparatus will often lead us astray. This is especially true on images which do not look like the ones we evolved for (and grew up looking at).
  3. The fact that seeing is believing is actually often a cognitive problem which needs to be overcome!


I can no longer find who said it a BOSC, but someone pointed out, insightfully, that a visualization is already an interpretation of the data, it may be wrong.

More often than not, I show you a picture of a cell, this is rarely raw data. The raw data is a big pixel array. By the time I’m showing it to you I’ve done the following:

  1. Chosen an example to show.
  2. Often projected the data from 3D to a 2D representation
  3. Tweaked contrast.

Point 1 is the biggest culprit here: the selection of which cell to image and show can be an incredibly biased process (even unconsciously biased, of course).

However, even tweaks to the way that the projection is performed and to the contrast can highlight or hide important details (as someone with a lot of experience playing with images, I can tell you that there is a lot of space for “highlighting what you want to show”). In the newer methods (super-resolution type methods), this is even worse: the “picture” you see is already the output of a big processing pipeline.


I’m not even thinking about the effects of the tagging protocols, which introduces their own artifacts. But we, humans, often make the mistake of saying things like “this is an image of protein A in cell type B” instead of “this is an image of a chimeric protein which includes the sequence of A, with a strong promoter in cell type B”.


We know that these artifacts and biases are there, of course. But we believe the images. And this can be a problem because humans are not actually all that great at image analysis.

Seeing is believing, which too often means that we suspend our disbelief (or, as we scientists, like to say: we suspend our skepticism). This is not a recipe for good science.

Update: On twitter, Jim Procter (@foreveremain), points out a great example: the story of the salmon fMRI: we can see it, but we shouldn’t believe it.

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